SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 976–1000 of 3304 papers

TitleStatusHype
Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches—0
Master's Thesis: Out-of-distribution Detection with Energy-based ModelsCode0
SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality ReductionCode0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
Capacity Analysis of Vector Symbolic Architectures—0
Online Kernel Sliced Inverse Regression—0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression—0
Information loss from dimensionality reduction in 5D-Gaussian spectral data—0
Using Topological Data Analysis to classify Encrypted Bits—0
DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and CheckingCode0
Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) dataCode0
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI—0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
ImMesh: An Immediate LiDAR Localization and Meshing FrameworkCode2
Fast conformational clustering of extensive molecular dynamics simulation dataCode1
Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization—0
Latent Autoregressive Source SeparationCode1
Topologically Regularized Data EmbeddingsCode0
Principal Component Analysis in Space FormsCode1
EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulationCode0
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified